Turning Data Into Actionable Insights: How Digital Marketers Can Interplay With Art And Science In Data Discovery Process?

The data analysis process is as much an art form as it is a science.

This is not an easy concept for some digital marketers to wrap their heads around. What is artistic about number-crunching and discovering correlations? Isn’t it just statistics and data science?

These marketers are not entirely wrong, but their view is short-sighted. If you only rely on math and data science, you won’t be able to turn your data into actionable insights consistently.

You’ll also have problems communicating and reporting your data findings to others, such as stakeholders or clients.

So, what is the role of art in the data analysis process? Your data holds valuable stories and insights that you need to improve your strategies. These insights are obscured by the size and complexity of the data itself.

When all of the practical, observational, systematic and other science-dependent activities are done, art transforms the raw numbers into actionable and viewable insights.

In other words, art translates your data into a format that the masses can quickly and easily understand.

This act of charting and visualizing your data is crucial in the data analysis process that should not be forgotten.

In this discussion, we’ll look at where this crossroads between art and science exists in data analysis. You’ll learn how to effectively incorporate both disciplines in your analysis and obtain actionable and complete insights that are ready for use.

Understanding The Relationship Between Art And Data Science

“Seeing is believing.”

This adage is at the core of why art has a role in the data analysis process.

Data tells you what’s going on, but artful visualizations show you what’s happening with your data.

That’s a significant difference.

Have you ever had to explain what something looks like because you didn’t have a picture available? Not only is this slower than merely showing someone the picture, but it is also less accurate.

Let’s say you wanted to describe your house to someone. No matter how much time you spend on this activity, no matter how many details you provide, the listener will not have a perfect picture in their head.

With just one image, they have a complete idea of what your house looks like. They see every detail you’ve described in microseconds. The same is true of your data.

Here is an exercise to demonstrate the point. Below is a table of data reflecting the favorite color of survey participants. Don’t scroll past the table until you can order the colors by most to least popular.

It took you a few seconds before you could compare the five colors and rank their popularity.

Now, let’s look at the same data reflected as a bar chart. You immediately see how each color ranks.

This is a simplistic example. It’s much more difficult to analyze a spreadsheet with hundreds of rows and columns, multiple dimensions and across different periods.

That’s the challenge facing marketers and that’s why art has a significant role in your analysis. Otherwise, it is hard to reach actionable insights in a timely fashion.

What Are Actionable Insights

The phrase “actionable insights” has been repeated many times throughout the start of this discussion. What is an actionable insight?

Insight is defined as “the capacity to gain an accurate and deep intuitive understanding of a person or thing.” Insights are valuable to marketers because they offer a deeper understanding of the essential aspects of marketing, such as audience behaviors, market trends and other details.

These clues are vital in understanding how to refine strategies and if your latest marketing efforts are working or not.

That said, not all insights are created equally; some are definitely more valuable than others. Many insights provide answers to key questions. Actionable insights, on the other hand, lead to action. They help you rethink the situation and find a new solution.

There are a few ways to measure the importance of your insights:

• Relevance: You have mountains of data, but not all of it is relevant to your marketing goals. You have to think critically about what types of decisions drive your goals and then focus on the insights that help you make those choices and lead to relevant actions.
• Specificity: There are broad insights that, while informative, don’t provide the specific details that help you act and grow. You want insights that provide direct and specific intel on what action to take next.
• Value: This is arguably the most crucial element — and also the hardest to measure. An insight can be specific and relevant, but if it doesn’t lead to something meaningful, then there isn’t much value. Sometimes, you can’t measure the value of an insight until after you’ve acted.

Understanding Analytics

Aside from defining actionable insights, it’s also vital that you understand analytics and the data analysis process.

Analytics is a systematic, mathematical approach to understanding data or statistics. This approach includes the discovery, collection, interpretation and communication of the most meaningful patterns and correlations within the data.

Computer programming, statistical computation and other mathematical algorithms can all play critical roles in analytics, as does charting and visualization software used to communicate findings.

This process is beneficial in digital marketing and advertising because there is a rich amount of information, metrics and other data involved. Large volumes of data lead to deeper and more interesting patterns.

It’s these patterns that help you identify the actionable insights that drive better decision-making.

By the explanation above, it’s easy to conclude that analytics is all science. The term itself comes from the use of information technology.

To reiterate, data analytics is science and art.

“Analytics” consists of many different components and stages. It starts with applications and systems that capture and organize data, but it ends with representing that information through visualization.

This is where the art enters the equation.

Data Analytics Challenges

Several challenges can impede analytics. Many of these obstacles can severely detract from your ability to extract valuable and actionable insights.

By identifying each analytics challenge, you’ll be better prepared to overcome them.

• Data Volume: More data must lead to more insights, right? This seems like sound logic, but the reality can play out differently. When you have lots of data, extracting insights becomes more of a challenge. There’s more stuff that you have to dig through to find what actually matters to your analysis.
• Data Variety: There are multiple types of data that you can collect and analyze. This diversity adds to the overall complexity of your data and subsequent analysis projects. Some of the data types you will need to contend with are structured, unstructured, natural language and media (audio, video and image data).
• Data Velocity: In the Digital Age, data is created rapidly and constantly. This means that your analysis also needs to happen quickly and consistently. If you’re taking too long to analyze the data properly, then you’ll only be capable of obtaining outdated insights. Ideally, you want to be proactive in your approach. This means tracking changes as they happen or before they occur, instead of reacting to shifts that have already passed.
• Data Veracity: Even if your analysis is perfect and you can extract insights, it can be an unrewarding pursuit if the data you’re analyzing is inaccurate or comes from unverified sources. Accuracy is everything and you should be skeptical of data until you know it can be trusted. When decisions are made based on unverified or inaccurate information, it can lead to disastrous results.

Ideally, you should look for three key characteristics in the data you’re analyzing.

• In-Time: Speed is a necessity. You should have data platforms that offer you the information you need, when you need it, with no waiting. This will help you eliminate costly hours in between each step of the process.
• Curated: Before working with any data, it should be cleaned, filtered and verified. This curation process is one of the most time-consuming steps in the process, but it will pay off huge when it comes to your insights’ value and quality.
• Right: To ensure relevant insights and remove potential distracting information, you only want to analyze the right This requires you to think critically about what you’re hoping to test or discover and then sticking exclusively to the data required to answer that hypothesis.

Where Digital Marketers Can Go Wrong In Their Analysis

As a digital marketer, you have a heavy workload. It is your job to identify customer needs and then determine the best course of action to meet those needs. You also have to promote your company, product and/or services through the right channels, while responding to changes in the market.

Insights help you understand the potential problems and seize valuable opportunities to further your goals. However, your skills and expertise play a significant role in the analysis process. Ultimately, it is up to you to know what is relevant, valuable and meaningful, especially in reporting.

This is again where art and science collide in your analysis. You need to have a mixture of these two diverse qualities. On the one side, you need to excel in mathematical understanding, but you also need to have the reverse side’s creativity and even curiosity.

Creativity doesn’t just serve you during the reporting phase, but also in selecting what to explore. If you want to unlock the hidden opportunities in your data, you can’t stay in the same line of thinking.

You have to go outside the box and turn over new rocks. You never know where a ground-breaking insight may be hiding.

Even AI will only get you so far. It’s up to you to choose which insights are most interesting and have the greatest potential reward.

It is often difficult for organizations to find a marketer with both unique and contrasting skill sets.

Discovering The Right Data Analysis Process

To overcome the challenges of analysis and your role in the process, you need the right approach. Data analysis has to be ongoing. If you don’t have the right plan in place, this persistence will become a fatiguing detriment to your workflow.

This section will outline tips for your data analysis process that you can use over and over again.

Stage 1: Measure The Right Things

Before you jump into your data, you need to know what matters most to your business and its campaigns.

You can read every digital marketing how-to guide in the world, but, unfortunately, they can’t tell you what you should be measuring because every business is different.

There is no one-size-fits-all, universal solution.

If you can’t readily answer what matters to your company, start by answering some of the following questions:

• Which channels generate the majority of your conversions?
• Which devices and how many do customers use before purchasing or converting?
• What are your conversion ratios for different products and categories? In other words, how many people look versus how many people buy and what do they buy?
• Where are your weak points? What are the pages on your site or places in your pipeline where customers drop off?
• What are your best landing pages? What landing pages are your worst?

When you can understand what matters to customers, you can determine what matters to your organization.

Stage 2: Consult Your Stakeholders

In some scenarios, knowing what matters most and what to measure requires you to consult your stakeholders.

If you’re working in the marketing/advertising department for a company, your stakeholders are your bosses and upper management. For marketing agencies, the stakeholder is the client.

Stakeholders can even include your team members. Anyone that has an interest in the success of your marketing and advertising can be considered a stakeholder.

You should work with your stakeholders and understand their most pressing questions and concerns. Then, use their queries to inform your analysis efforts.

By collaborating with stakeholders, you may discover those out-of-the-box questions and ideas that can lead to a creative, artful analysis approach and deeper insights.

Stage 3: Segment Your Audience

To help simplify your data analysis, look at ways to segment your audience. When you can group audience members with shared attributes, it’s easier to dig deeper into the data and the insights.

For instance, you might have 100,000 total customers. That’s a lot of data to analyze and manage at once — it’s like trying to eat a 12-ounce steak whole.

Segmenting your audience is the act of cutting those 100,000 customers into smaller, logical and more bite-sized pieces that are easier to manage and analyze.

You can group audiences by demographics (age, gender, location, income level, etc.), funnel position, time, device type and more.

Identifying these key segments will help you understand how different groups of customers behave and interact with your marketing and PPC content.

Then, you can focus on discovering insights specific to your most important audience segments.

Step 4: Create Clear Charts To Convey Results

When your analysis is finished and you’ve reached the end of the road on the data side, your focus shifts to the art side of things. This means choosing the right way to present and visualize your data.

Remember, when you present only tables and numbers, it can be too demanding on your audience. You want people to be able to reach the same conclusions that you found when analyzing the data.

This is not easy.

As a marketer, you work with this data on a daily basis. You know what all of the metrics mean and why they matter. You have a sense of where performance was last month, which makes it easier to comprehend whether things are trending up or down this month,

Your stakeholders, however, do not have this same level of familiarity with the data.

Your PPC reports and charts need to bridge this gap and provide viewers with all the context and background information they need to understand the results and insights at the same level you do.

Thus, you need to make every chart and report as clear as possible. This may mean overcoming your cognitive biases. You don’t want to leave out important details because you assume they are apparent!

Step 5: Collaborate With Smart People And Smart Tools

Your analysis process should not be a one-man or one-woman show. You want to collaborate with others.

By involving team members and colleagues in your analysis process, you prevent yourself from falling into old patterns and habits that lead to the same types of insights.

Again, out-of-the-box thinking is almost mandatory if you want those ultra-valuable and actionable insights that have the potential to change your entire approach.

It’s also a wise strategy to work with smart AI tools. These solutions are designed to overcome all of the common challenges associated with analytics.

AI can analyze large stores of data in a fraction of the time that it would take you. This dramatically hastens your analysis process.

Not to mention, AI can act as an additional team member. If you’re managing your marketing and PPC strategies on your own, AI can provide insights and suggestions that you may not have already thought of.

PPC Signal Brings Art, Science And Data Together

You understand the correlation between art, science and data analysis. Now the question is, ‘how do you bring it all together?’ How do you overcome the challenges associated with data analysis and reporting data? The answer is PPC Signal.

PPC Signal uncovers the heroes and villains of your PPC campaigns — your greatest opportunities and your looming risks.

It invokes both art and deep science to investigate all areas of your campaigns, even the parts that you might normally overlook.

The prime advantage of using PPC Signal is receiving timely updates on different actions that you can take to optimize your PPC strategies and campaigns.

Thanks to the power of machine learning and AI-backed algorithms, PPC Signal is capable of monitoring the always-changing data from your campaigns.

When noteworthy trends, shifts, patterns, outliers and other events occur within the data, PPC Signal alerts you. This may be a positive or negative shift.

Each signal that the system shares with you is packed with data. You’ll see what’s changing, by how much, when the shift started, what direction, which campaign components are affected and more.

After clicking on a signal, you can Explore further by clicking the appropriate button. This will show you a much more detailed trend line.

You can also click to see a tabular view of the same data. This allows you to dig deeper into the trends and shifts and see what’s really going on in your data.

In this example, PPC Signal identified a significant trend affecting the cost per conversion for a particular campaign. Cost per conversion values went from $57.60 to $798.30. That’s a staggering increase of 1,285%!

The size and impact of this trend means that you would undoubtedly detect it on your own — eventually. However, each day that it goes unnoticed would cost you hundreds of dollars.

Timing is everything with PPC management; PPC Signal allows you to act correctly and promptly to all changes.

Once you identify a change, PPC Signal recommends specific actions you can take to mitigate the risk or seize the opportunity.

Wrap Up

Turning your data into actionable insights is not an easy feat. It requires a lot more than an acuity for data and numbers. You need to have a creative, artistic approach to not only how you visualize and charting your findings, but also in planning and executing your analyses.

You need to think outside your regular routines and processes to find fresh angles and discover new ways to engage audiences with paid and unpaid marketing messages.

When you can correctly blend the science and art of data analysis, obtaining high-value, actionable insights becomes easy.

With PPC Signal, acquiring actionable insights becomes part of your daily routine.